Machine learning in Python with scikit-learn

Netherlands eScience Center

March 11 - 12, 2025

9:30 - 17:00 CET

Instructors: Malte Luken, Sven van der Burg, Carsten Schnober

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General Information

The eScience Center offers a range of workshops and training courses, aimed at PhD candidates and other researchers or research software engineers. We organize workshops covering digital skills needed to put reproducible research into practice. These include online collaboration, reproducible code and good programming practices. We also offer more advanced workshops such as GPU Programming, Parallel Programming, Image Processing and Deep Learning.

This hands-on workshop will provide you with the basics of machine learning using Python.

Machine learning is the field devoted to methods and algorithms that ‘learn’ from data. It can be applied to a vast range of different domains, from linguistics to physics and from medical imaging to history.

This workshop covers the basics of machine learning in a practical and hands-on manner, so that upon completion, you will be able to train your first machine learning models and understand what next steps to take to improve them.

We start with data exploration and prepare the data so that it is suitable for machine learning. Then we learn how to train a model on the data using scikit-learn. We learn how to select the best model from the trained models and how to use different machine learning models (like linear regression, logistic regression, and decision tree models). Finally, we discuss some of the best practices when starting your own machine learning project.

Who:

The course aims to be accessible without a strong technical background.

This course is for you if:

You have basic knowledge of Python programming : defining variables, writing functions, importing modules. Some prior experience with the NumPy, pandas and Matplotlib libraries is recommended but not required.
You want to learn how to setup a full machine learning pipeline in Python for various machine learning tasks.
You want to get an intuition of basic machine learning concepts, such as train-test data splits, model training and evaluation, different machine learning algorithms, overfitting/underfitting, bias-variance trade-off.

This course is not for you if:

You already have experience with machine learning or its concepts, this is really an introduction for people that have never done machine learning or only just started but need more guidance.
You want to get a solid mathematical understanding of machine learning theory. This course aims to quickly get participants comfortable applying machine learning in practice, we therefore only cover the basis of theoretical concepts without going into depth.
You want to learn about deep learning
You want to learn about more advanced data preprocessing, like data cleaning, handling missing values etcetera. We only cover the basics of data preprocessing that are needed to setup a machine learning pipeline.

Also have a look at the syllabus to see what topics we will cover.

If you are uncertain whether this course is for you, please send us an email.

Where: Science Park 402, 1098 XH Amsterdam. Get directions with OpenStreetMap or Google Maps.

When: March 11 - 12, 2025, 9:30 - 17:00 CET.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:

The room is wheelchair / scooter accessible.
Accessible restrooms are available.

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Workshop files: You will find all slides, notebooks, archived collaborative documents, and other relevant files in the files folder of the workshop website repository after the workshop.

Contact: Please email or training@esciencecenter.nl for more information.

Code of Conduct

Participants are expected to follow these guidelines:

Use welcoming and inclusive language
Be respectful of different viewpoints and experiences
Gracefully accept constructive criticism
Focus on what is best for the community
Show courtesy and respect towards other community members

Syllabus

Machine learning concepts

What is machine learning?
Different types of machine learning
Big picture of machine learning models
General pipeline

The predictive modeling pipeline

Tabular data exploration
Fitting a scikit-learn model on numerical data
Handling categorical data

Selecting the best model

Overfitting and underfitting
Validation and learning curves
Bias versus variance trade-off

Machine learning algorithms

Intuitions on linear models
Intuitions on tree-based models

Machine learning best practices

Data hygiene
Correct evaluation
How to keep your machine learning project organised

Schedule

Day 1

local Amsterdam time	what
09:30	Welcome and icebreaker
09:30	Introduction to machine learning
10:30	Break
10:40	Tabular data exploration
11:30	Break
11:40	Fitting a scikit-learn model on numerical data
12:30	Lunch Break
13:30	Fitting a scikit-learn model on numerical data
14:30	Break
14:40	Fitting a scikit-learn model on numerical data
15:30	Break
15:40	Fitting a scikit-learn model on numerical data
16:15	Wrap-up
16:30	END

Day 2

local Amsterdam time	what
09:30	Welcome and recap
09:45	Handling categorical data
10:30	Break
10:40	Combining numerical and categorical data
11:30	Break
11:40	Intuitions on decision trees
12:00	Overfitting and underfitting
12:30	Lunch Break
13:30	Bias versus variance trade-off
14:00	Advanced topics
14:30	Break
14:40	Try out learned skills on US census dataset
15:30	Break
15:40	Machine learning best practices; Q&A
16:15	Wrap-up & Post-workshop Survey
16:30	Drinks

All times in the schedule are in the CET timezone.

Setup

To participate in this workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

Software setup

It is important that you setup everything on your laptop before the start of the course. This includes installing a Python environment and downloading the necessary files. Please follow these setup instructions. Send us an email if you encounter any problems.